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Performance of Eigenvaluebased Signal Detectors with Known and Unknown Noise Level
"... Abstract—In this paper we consider signal detection in cognitive radio networks, under a nonparametric, multisensor detection scenario, and compare the cases of known and unknown noise level. The analysis is focused on two eigenvaluebased methods, namely Roy’s largest root test, which requires kn ..."
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Cited by 13 (5 self)
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Abstract—In this paper we consider signal detection in cognitive radio networks, under a nonparametric, multisensor detection scenario, and compare the cases of known and unknown noise level. The analysis is focused on two eigenvaluebased methods, namely Roy’s largest root test, which requires knowledge of the noise variance, and the generalized likelihood ratio test, which can be interpreted as a test of the largest eigenvalue vs. a maximumlikelihood estimate of the noise variance. The detection performance of the two considered methods is expressed by closedform analytical formulas, shown to be accurate even for small number of sensors and samples. We then derive an expression of the gap between the two detectors in terms of the signaltonoise ratio of the signal to be detected, and we identify critical settings where this gap is significant (e.g., small number of sensors and signal strength). Our results thus provide a measure of the impact of noise level knowledge and highlight the importance of accurate noise estimation. I.
Blind spectrum sensing by information theoretic criteria for cognitive radios
 IEEE Trans. Veh. Technol
, 2010
"... Abstract—Spectrum sensing is a fundamental and critical issue for opportunistic spectrum access in cognitive radio networks. Among the many spectrumsensing methods, the information theoretic criteria (ITC)based method is a promising blind method that can reliably detect the primary users while re ..."
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Cited by 8 (2 self)
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Abstract—Spectrum sensing is a fundamental and critical issue for opportunistic spectrum access in cognitive radio networks. Among the many spectrumsensing methods, the information theoretic criteria (ITC)based method is a promising blind method that can reliably detect the primary users while requiring little prior information. In this paper, we provide an intensive treatment on the ITC sensing method. To this end, we first introduce a new overdetermined channel model constructed by applying multiple antennas or oversampling at the secondary user to make ITC applicable. Then, a simplified ITC sensing algorithm is introduced, which needs to compute and compare only two decision values. Compared with the original ITC sensing algorithm, the simplified algorithm significantly reduces the computational complexity with no loss in performance. Applying the recent advances in random matrix theory, we then derive closedform expressions to tightly approximate both the probability of false alarm and the probability of detection. Based on the insight derived from the analytical study, we further present a generalized ITC sensing algorithm that can provide a flexible tradeoff between the probability of detection and the probability of false alarm. Finally, comprehensive simulations are carried out to evaluate the performance of the proposed ITC sensing algorithms. Results show that they considerably outperform other blind spectrumsensing methods in certain cases. Index Terms—Cognitive radio (CR) networks, information theoretic criteria (ITC), random matrix theory, spectrum sensing. I.
On the performance of spectrum sensing algorithms using multiple antennas
 IEEE Trans. Wireless Communications
, 2010
"... Abstract—In recent years, some spectrum sensing algorithms using multiple antennas, such as the eigenvalue based detection (EBD), have attracted a lot of attention. In this paper, we are interested in deriving the asymptotic distributions of the test statistics of the EBD algorithms. Two EBD algorit ..."
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Cited by 6 (2 self)
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Abstract—In recent years, some spectrum sensing algorithms using multiple antennas, such as the eigenvalue based detection (EBD), have attracted a lot of attention. In this paper, we are interested in deriving the asymptotic distributions of the test statistics of the EBD algorithms. Two EBD algorithms using sample covariance matrices are considered: maximum eigenvalue detection (MED) and condition number detection (CND). The earlier studies usually assume that the number of antennas (K) and the number of samples (N) are both large, thus random matrix theory (RMT) can be used to derive the asymptotic distributions of the maximum and minimum eigenvalues of the sample covariance matrices. While assuming the number of antennas being large simplifies the derivations, in practice, the number of antennas equipped at a single secondary user is usually small, say 2 or 3, and once designed, this antenna number is fixed. Thus in this paper, our objective is to derive the asymptotic distributions of the eigenvalues and condition numbers of the sample covariance matrices for any fixed K but large N, from which the probability of detection and probability of false alarm can be obtained. The proposed methodology can also be used to analyze the performance of other EBD algorithms. Finally, computer simulations are presented to validate the accuracy of the derived results.
1 GLRTBased Spectrum Sensing with Blindly Learned Feature under Rank1 Assumption
"... Abstract—Using signal feature as the prior knowledge can improve spectrum sensing performance. In this paper, we consider signal feature as the leading eigenvector (rank1 information) extracted from received signal’s sample covariance matrix. Via realworld data and hardware experiments, we are abl ..."
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Cited by 6 (4 self)
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Abstract—Using signal feature as the prior knowledge can improve spectrum sensing performance. In this paper, we consider signal feature as the leading eigenvector (rank1 information) extracted from received signal’s sample covariance matrix. Via realworld data and hardware experiments, we are able to demonstrate that such a feature can be learned blindly and it can be used to improve spectrum sensing performance. We derive several generalized likelihood ratio test (GLRT) based algorithms considering signal feature as the prior knowledge under rank1 assumption. The performances of the new algorithms are compared with other stateoftheart covariance matrix based spectrum sensing algorithms via Monte Carlo simulations. Both synthesized rank1 signal and realworld digital TV (DTV) data are used in the simulations. In general, our GLRTbased algorithms have better detection performances, and the algorithms using signal feature as the prior knowledge have better performances than the algorithms without any prior knowledge. Index Terms—Spectrum sensing, cognitive radio (CR), generalized likelihood ratio test (GLRT), hardware. I.
Multiantenna GLR detection of rankone signals with known power spectrum in white noise with unknown spatial correlation
 IEEE Trans. Signal Process
, 2012
"... Abstract—Multipleantenna detection of a Gaussian signal with spatial rank one in temporally white Gaussian noise with arbitrary and unknown spatial covariance is considered. This is motivated by spectrum sensing problems in the context of dynamic spectrum access in which several secondary networks ..."
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Cited by 6 (1 self)
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Abstract—Multipleantenna detection of a Gaussian signal with spatial rank one in temporally white Gaussian noise with arbitrary and unknown spatial covariance is considered. This is motivated by spectrum sensing problems in the context of dynamic spectrum access in which several secondary networks coexist but do not cooperate, creating a background of spatially correlated broadband interference. When the temporal correlation of the signal of interest is assumed known up to a scale factor, the corresponding Generalized Likelihood Ratio Test is shown to yield a scalar optimization problem. Closedform expressions of the test are obtained for the general signal spectrum case in the low signaltonoise ratio (SNR) regime, as well as for signals with binaryvalued power spectrum in arbitrary SNR. The two resulting detectors turn out to be equivalent. An asymptotic approximation to the test distribution for the lowSNR regime is derived, closely matching empirical results from spectrum sensing simulation experiments. Index Terms—Capon beamformer, cognitive radio, correlated noise, detection, GLR test, multiantenna array, noise uncertainty, spectral flatness measure, spectrum sensing. I.
Spectrum sensing for OFDM signals using pilot induced autocorrelations,” in
 IEEE J. Select. Areas Commun
, 2013
"... Abstract—Orthogonal frequency division multiplex (OFDM) has been widely used in various wireless communications systems. Thus the detection of OFDM signals is of significant importance in cognitive radio and other spectrum sharing systems. A common feature of OFDM in many popular standards is that s ..."
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Cited by 5 (0 self)
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Abstract—Orthogonal frequency division multiplex (OFDM) has been widely used in various wireless communications systems. Thus the detection of OFDM signals is of significant importance in cognitive radio and other spectrum sharing systems. A common feature of OFDM in many popular standards is that some pilot subcarriers repeat periodically after certain OFDM blocks. In this paper, sensing methods for OFDM signals are proposed by using such repetition structure of the pilots. Firstly, special properties for the autocorrelation (AC) of the received signals are identified, from which the optimal likelihood ratio test (LRT) is derived. However, this method requires the knowledge of channel information, carrier frequency offset (CFO) and noise power. To make the LRT method practical, we then propose an approximated LRT (ALRT) method that does not rely on the channel information and noise power, thus the CFO is the only remaining obstacle to the ALRT. To handle the problem, we propose a method to estimate the composite CFO and compensate its effect in the AC using multiple taps of ACs of the received signals. Computer simulations have shown that the proposed sensing methods are robust to frequency offset, noise power uncertainty, time delay uncertainty, and frequency selectiveness of the channel. Index Terms—Cognitive radio, spectrum sensing, OFDM, covariance, autocorrelation, cyclostationary, statistics, detection, robust
Detecting passive eavesdroppers in the MIMO wiretap channel
 in Proc. IEEE ICASSP
, 2012
"... The MIMO wiretap channel comprises a passive eavesdropper that attempts to intercept communications between an authorized transmitterreceiver pair, with each node being equipped with multiple antennas. In a dynamic network, it is imperative that the presence of a passive eavesdropper be determined ..."
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Cited by 4 (2 self)
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The MIMO wiretap channel comprises a passive eavesdropper that attempts to intercept communications between an authorized transmitterreceiver pair, with each node being equipped with multiple antennas. In a dynamic network, it is imperative that the presence of a passive eavesdropper be determined before the transmitter can deploy robust secrecyencoding schemes as a countermeasure. This is a difficult task in general, since by definition the eavesdropper is passive and never transmits. In this work we adopt a method that allows the legitimate nodes to detect the passive eavesdropper from the local oscillator power that is inadvertently leaked from its RF front end. We examine the performance of noncoherent energy detection as well as optimal coherent detection schemes. We then show how the proposed detectors allow the legitimate nodes to increase the MIMO secrecy rate of the channel. Index Terms — MIMO wiretap channel, passive eavesdropper, energy detection 1.
Spectrum Sensing Using Correlated Receiving Multiple Antennas in Cognitive Radios
"... Abstract—In this paper, we address the problem of multiantenna spectrum sensing in Cognitive Radios (CRs) by considering the correlation between the received channels at different antennas. First, we derive the optimum genieaided detector which assumes perfect knowledge of the antenna correlation ..."
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Abstract—In this paper, we address the problem of multiantenna spectrum sensing in Cognitive Radios (CRs) by considering the correlation between the received channels at different antennas. First, we derive the optimum genieaided detector which assumes perfect knowledge of the antenna correlation coefficients, Primary User (PU) signal power and noise variance. This is used as a benchmark for comparing with more practical detectors when some or all of these parameters are unknown to the Secondary User (SU). Two scenarios are considered: 1) the antenna correlation coefficients and PU signal power are unknown to the SU; 2) the antenna correlation coefficients, PU signal power and noise variance are unknown to the SU. To derive suboptimum detectors for these two scenarios, we apply the Rao test, an asymptotically equivalent test to the Generalized Likelihood Ratio Test (GLRT) that does not require the Maximum Likelihood (ML) estimates of unknown parameters. Additionally, we calculate analytical approximations to the detection and falsealarm probabilities of the proposed detectors and verify them with MonteCarlo simulations. The simulation results show that these new detectors outperform several recently proposed detectors for CR using multiple antennas. Index Terms—Cognitive radio, spectrum sensing, multiple antennas, Rao test, antenna correlations, Fisher information matrix, noise variance mismatch, antenna array.
Prescient beamforming for multiuser interweave cognitive radio networks
 in Proc. 2011 IEEE Int. Workshop Comput. Advances in MultiSensor Adaptive Process
, 2011
"... Abstract—This work investigates a fundamentally novel interweave cognitive radio network where the primary transmitter takes a proactive approach towards improving the accuracy of the spectrum sensing outcomes at the secondary users (SUs). For the singleprimaryreceiver scenario considered here, t ..."
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Abstract—This work investigates a fundamentally novel interweave cognitive radio network where the primary transmitter takes a proactive approach towards improving the accuracy of the spectrum sensing outcomes at the secondary users (SUs). For the singleprimaryreceiver scenario considered here, the multiantenna primary user constructs its transmit beamforming vector so as to increase the detection probability at the SUs while meeting a desired QualityofService (QoS) target on its own link, by exploiting partial channel state information of the SUs. The objective of such a proactive approach, which we refer to as prescient precoding, is to minimize the probability of interference from SUs at the primary receiver due to imperfect spectrum sensing in fading channels. Numerical results are presented to verify the advantages of the proposed prescient transmission techniques for noncooperative spectrum sensing scenarios. I.
1 Improving the SensingThroughput Tradeoff for Cognitive Radios in Rayleigh Fading Channels
"... Abstract—Inband spectrum sensing in overlay cognitive radio networks requires that the secondary users (SU) periodically suspend their communication in order to determine whether the primary user (PU) has started to utilize the channel. In contrast, in spectrum monitoring the SU can detect the emer ..."
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Abstract—Inband spectrum sensing in overlay cognitive radio networks requires that the secondary users (SU) periodically suspend their communication in order to determine whether the primary user (PU) has started to utilize the channel. In contrast, in spectrum monitoring the SU can detect the emergence of the PU from its own receiver statistics such as receiver error count (REC). Previously it is shown that in AWGN channels, a hybrid spectrum sensing/spectrum monitoring system significantly improves channel utilization of the SUs and detection delay of the PUs. In this paper we investigate the problem of spectrum monitoring in the presence of fading where the SU employs diversity combining to mitigate the channel fading effects. We show that a decision statistic based on the REC alone does not provide a good performance. Next we introduce new decision statistics based on the REC and the combiner coefficients. It is shown that the new decision statistic achieves significant improvement in the case of maximal ratio combining (MRC). However, for equal gain combining and selection combining the inclusion of combiner coefficients does not improve the performance over REC alone. In the case of MRC we evaluate the receiver operating characteristics from analysis and compare the results with those from simulations using a BCH code as well as a convolutional code. The results show a close match between analysis and simulation results. Channel utilization and detection delay are evaluated from simulations which show that with MRC and the proposed decision statistic, the hybrid spectrum sensing/spectrum monitoring system significantly outperforms spectrum sensing alone. Index Terms—Spectrum sensing, spectrum monitoring, channel utilization, detection delay, fading channel, diversity combining. I.